The Data Talent Race: Insights to Attract & Retain Top Professionals¶
The rapid expansion of the tech industry has intensified competition for top data professionals, making it more challenging for companies to attract and retain the right talent. Roles such as Data Scientists, Machine Learning Engineers, and AI Specialists are pivotal in driving innovation and fueling data-driven decision-making. Yet, evolving skill demands, shifting work preferences, and rising compensation expectations add complexity to hiring strategies.
This report analyzes global salary data to uncover key compensation trends based on job roles, experience levels, work models, and company sizes. Armed with these insights, businesses can refine their hiring strategies, optimize salary structures, and position themselves as leading employers in the competitive data job market.
📌 Key Insights at a Glance¶
📊 Survey Insights
- 57,194 tech professionals surveyed
- 90.7% work for USA based companies, with 90.6% of employees residing in USA
📈 Tech Job Market Boom
- 448% increase in tech job openings from 2023 to 2024
- Data Scientists (19%) and Data Analysts (17.4%) dominate the job market
💰 Salary Growth & Compensation Trends
- Average annual salary jumped by 34.3% in 2022 to 134K, continuing its rise in 2023 & 2024
- 7 of the top 10 highest-paying roles in 2024 belong to Machine Learning, with 5 of them in managerial positions
- Machine Learning roles lead as the highest-paying category, averaging 192K per year
- 2024 salary distribution is more balanced, with a smaller gap between the highest and lowest-paying regions
🏢 Impact of Company Size on Salaries
- Larger companies consistently offer higher, more stable salaries
- Moving from entry-level in a small company to mid-level in a medium-sized company can double salary earnings
👨💻 Employment Type & Work Model Preferences
- Full-time roles dominate 99.6% of job opportunities and offer the highest salaries
- Onsite jobs pay slightly higher than remote roles, but the gap is minimal and offset by work flexibility
🌍 Regional Salary Trends
- USA is the only country consistently appearing in the top 10 salary-paying locations across 5 years
- 2023 saw a record-breaking salary peak—for the first time, salaries crossed 300K.
- Whereas in previous years, no location exceeded 200K
Tech Hiring Boom: Explosive Growth in Job Opportunities¶
The tech industry has experienced an unprecedented surge in job opportunities, particularly from 2022 to 2024. After a slow rise in previous years, the number of tech jobs has skyrocketed, signaling a high demand for skilled professionals. This surge reflects the industry's rapid expansion and digital transformation.
Job opportunities remained relatively low from 2020 to 2022, showing only a gradual increase. The number of jobs started to increase more rapidly in 2023, marking a turning point for the industry.The most striking trend is the massive spike in 2024, where job openings quadrupled compared to previous years.
This rapid growth is likely driven by increased reliance on tech solutions, AI advancements, and a growing demand for data-driven roles. Data Scientists and Data Analysts dominating the job market. Data Engineers and Software Engineers also hold strong positions, reflecting the critical need for data infrastructure and software development. Meanwhile, Machine Learning Engineers and Managers see lower but notable demand, while Consultants appear to have the least job opportunities
Tech Salaries on the Rise: A Five-Year Growth Trend¶
As seen in the job opportunities graph, the number of tech jobs has skyrocketed. This correlates directly with the salary increase shown in this graph, as companies compete for top talent. The significant rise in salaries post-2021 by 34.3 % reflects the high demand for skilled professionals, pushing employers to offer more competitive compensation to attract and retain employees in an expanding market.
💰 The Highest-Paying Job Titles & Categories in 2024 – Who’s Leading the Salary Race?¶
Head of Machine Learning and Applied AI ML Lead leading the pack with salaries exceeding $300K. Engineering roles, particularly Engineering Managers and ML Performance Engineers, also command high compensation. Leadership positions in AI, such as Head of AI and Director of Machine Learning, continue to see strong salary packages, reflecting the increasing demand for AI expertise.
The Machine Learning Engineer (ML Engineer) role stands out as the highest-paying job category in 2024, reinforcing the trend observed in top-paying job titles where AI and ML leadership dominated. Software Engineers and Managers also command strong salaries, showcasing the high demand for engineering and leadership expertise. Data Scientists and Data Engineers continue to be well-compensated, while Data Analysts and Consultants rank lower in comparison.
Salary Trends Across Employment Types (2020-2024) 💰¶
Companies offer different employment types to optimize costs, flexibility, and workforce needs. The four common types—Full-time, Part-time, Contract, and Freelance—serve different business goals and employee preferences. Ultimately, companies balance cost and expertise, while workers choose between stability and flexibility.
- Full-time employment remains the most lucrative and stable career choice, showing continuous salary growth.
- Part-time employment has experienced steady growth, though it remains significantly lower than full-time positions.
- Contract roles show extreme salary spikes in certain years (notably 2021), indicating potential high-paying (for Specialized Skills) but less predictable opportunities.
- Freelance salaries remain the lowest, with minimal fluctuations, possibly due to variable work availability and project-based pay structures.
🌍 Global Salary Trends: Top 10 Company Locations Over Time¶
As the job market evolves, salaries across different countries fluctuate, reflecting economic trends, industry demands, and regional growth.
This analysis uncovers the top-paying company locations over the past five years.
- United Kingdom stands out as the only country consistently appearing in the top 10 company locations by average salary across all five years.
- 2023 saw a record-breaking salary peak, with the maximum average salary reaching 300K USD, a significant jump compared to previous years, where no location exceeded 200K USD.
- In 2024, salary distribution across the top 10 locations is more uniform, with a narrow gap between the highest and lowest average salaries, indicating a more balanced global pay scale.
📈 How Experience and Company Size Shape Your Paycheck!¶
Does experience guarantee a higher salary? Does working at a large company mean bigger paychecks? let's explores how experience level (from entry-level to executive) and company size (small, medium, and large) impact salary trends. Understanding these dynamics can help professionals make strategic career choices and maximize earnings.
How Seniority Impacts Salary Growth in Tech¶
Salary is not just a reflection of job title or company size—it is heavily influenced by seniority level. As professionals progress from entry-level (EN) to mid-level (MI), senior (SE), and expert (EX) roles, their earning potential increases significantly.
The data shows a clear upward trend in salaries with experience, with senior and expert professionals commanding the highest pay. Additionally, the salary gap between junior and senior roles has widened in recent years, highlighting the growing demand for experienced talent.
There is a clear positive correlation between experience level and salary (Higher Experience = Higher Salaries) .
The salary growth is more significant for Senior (SE) and Mid-Level (MI) levels, indicating increasing demand for highly skilled professionals. Widening Salary Gap Between Experience Levels
Expert level (EX) salaries showing low growth but the error bars represent larger variability, especially in earlier years (2020-2021).This indicates a wide range of salaries for senior roles, likely due to variations in industry, job function, and company size.However, as the years progress, variability decreases, suggesting more standardized salary trends for senior professionals.
The gap between entry-level (EN) and Mid-Level (MI) salaries has widened over the years. In 2020, some entry-level roles had salaries approaching mid-level (MI) roles, but by 2024, there is a clear distinction between each level. This suggests that companies are willing to pay significantly more for expertise and leadership in recent years.
Entry-level (EN) salaries show the least variability and remain significantly lower than other levels.This indicates that junior roles have more predictable pay ranges, while salaries for experienced professionals vary based on negotiation power, industry demand, and specialization.
How Company Size Shapes Salary Trends¶
While experience is a key factor in determining salaries, it is far from the only one. Company size plays a crucial role in shaping compensation structures, with larger organizations often offering higher salaries, better benefits, and more structured career growth opportunities. Startups and smaller firms, on the other hand, may provide competitive salaries but often compensate with equity, flexibility, or unique perks.
Across all years, large companies (L) consistently offer higher median salaries than medium (M) and small (S) companies. The salary gap between company sizes is especially evident in recent years (2022–2024), where large firms show the highest pay.
The error bars indicate salary variability (range of salaries within each category). In 2020 and 2021, salaries in small and medium companies showed more fluctuation, suggesting less stability in compensation. In contrast, large companies show more consistent salary trends, especially in recent years.
While medium and large firms show a clear upward trend, small companies (S) seem to have stagnated at lower salary ranges, particularly from 2022 onwards. This could indicate that small firms struggle to compete with larger firms in salary offerings, potentially making talent retention harder.
2024 Salary Breakdown on Experience & Company Size¶
- Small companies (S) offer the lowest salaries across all experience levels, with high variability, indicating that skill level and negotiation power significantly impact earnings.
- Large companies (L) consistently provide the highest salaries across all experience levels, followed by medium-sized companies (M).
- Mid (MI) and Senior (SE) level employees see minimal salary differences between medium and large companies, suggesting intense competition to attract skilled professionals.
- Executive-level (EX) salaries show the highest variance, particularly in large companies, indicating greater earning potential but also increased unpredictability in compensation.
This analysis underscores the importance of both experience and company size in salary progression, helping professionals and job seekers align their career strategies for maximum financial growth.
Remote Work Choices and Salary Impact¶
Remote work has experienced a dynamic shift, from rapid adoption during the pandemic to a gradual return to onsite work. Companies initially embraced remote and hybrid models to maintain operations, but as the job market evolved, many organizations reconsidered their approach. While remote work provides flexibility and access to global talent, some companies argue that onsite collaboration enhances productivity and innovation.
This shift directly impacts salaries, with remote jobs often offering location-based pay adjustments and hybrid roles maintaining competitive wages.
- Remote work was dominant in 2020 and 2021, with hybrid and onsite models gradually increasing.
- 2022 marked a turning point, with a significant decline in hybrid roles and a rise in onsite work while remote role percentage didn't change.
- By 2023 and 2024, onsite work became the majority, suggesting a shift in companies preferences.
Salaries in remote roles may face standardization or reductions, while onsite jobs may regain negotiating power, particularly in competitive markets.
Remote, Hybrid, or Onsite: Which Pays the Most in 2024?¶
- Onsite roles generally offer the highest salaries, especially for machine learning engineers and mangerial positions.
- Remote and onsite salaries for Data Analysts and Data Scientists show negligible differences
- Machine Learning and Software Engineers have the highest earning potential, regardless of work mode.
- Hybrid roles tend to have the lowest pay
Craeting Model to Predict Salary in USD¶
1- Check Normality¶
- Check Normality for our target variable (salary_in_usd)
The salary distribution (salary_in_usd) exhibits right-skewness, indicating that it is not normally distributed. Since linear models—such as Linear Regression, Lasso, and Ridge—assume a normally distributed target variable, applying a log transformation is necessary to stabilize variance and improve model performance.
However, for tree-based models (e.g., Decision Trees, Random Forest, XGBoost), normalization is not required, as these models are robust to skewed distributions and do not rely on linear assumptions.
Therefore, for models based on linear regression techniques, we apply a log transformation to ensure better predictive accuracy, while for decision tree-based models, this step can be skipped.
2-Feature Selection and Encoding Strategy¶
Columns to Remove:¶
We will eliminate the following columns as they are either redundant or have been transformed into more useful features:
- work_year: The dataset only contains data for the year 2024, making this column irrelevant.
- job_title: Jobs have been categorized into broader groups in the newly created job_category column.
- company_location: Countries have been grouped into broader regions in the company_sub_region column.
- salary and salary_currency: Salaries have been standardized into a unified metric (salary_in_usd).
- employee_residence: This information is redundant as we will use company_location instead.
Selected Features:¶
The following columns will be used as features for model training:
experience_level, employment_type, remote_ratio, company_size, job_category, company_sub_region]
Feature Encoding Strategy:¶
From our previous exploratory analysis, we identified that most features are categorical and require encoding before training the model:
- Ordinal Encoding:
experience_level,employment_type, andcompany_sizehave a natural hierarchical order, making ordinal encoding the best approach.
- Categorical Encoding:
job_categoryandcompany_sub_regioneach have fewer than 10 unique values, allowing for multiple encoding options such as One-Hot Encoding, Label Encoding, or Frequency Encoding.
- Numerical Feature Scaling:
remote_ratio(with values 0, 50, and 100) will be standardized using Standard Scaling to ensure consistent feature scaling.
job_category, company_sub_region and experience_level are the most powerful feature to predict salary in USD
Due to the lack of continuous numerical features, the dataset primarily consists of discrete values after converting categorical variables into numerical form. This limitation affects the model's ability to capture underlying patterns, leading to a low performance score.
3- Model Enhancement¶
- Feature Engineering
Creating new feature vacancy_param combine job details
vacancy_param = experience_level + employment_type + company_size
- Target Encoding
Replacing categorical values with the mean of the target variable for each category, this is useful when a categorical feature has many unique values
We will use this encoding with job_title and company_location instead of lebel encoding
Now, we noticed that job_title_encoded, company_location_encoded and vacancy_param are the most power features
Lest check the impact of these feature on our model
The model's performance improved from R² = 0.30 to 0.38 due to target encoding and feature engineering.
These optimizations enhanced the model's ability to capture complex relationships, resulting in a 7% increase in R² score.
Let's Predict Salary Using Gradient Boosting Model¶
- As Gradient Boosting Model give us the highest score
New data to predict
- Here are predicted salary in USD after applying our model